Distributed and personalised social network privacy protection Online publication date: Tue, 22-Jan-2019
by Xiao-lin Zhang; Xiao-yu He; Fang-ming Yu; Li-xin Liu; Huan-xiang Zhang; Zhuo-lin Li
International Journal of High Performance Computing and Networking (IJHPCN), Vol. 13, No. 2, 2019
Abstract: Considering the privacy issues on social network, a variety of anonymous techniques have been proposed, but these techniques neglect some differences among individuals in their demand for privacy protection. With the development of internet technology, the number of social network individuals increases yearly, and network data are poised for a massive change in trends. Motivated by this, we specify three levels of privacy information for victim individuals and propose a personalised k-degree-m-label (PKDML) anonymity model. Furthermore, we design and implement a distributed and personalised k-degree-m-lable (DPKDML) anonymisation algorithm, which takes advantage of the 'vertex-centric' GraphX programming model to complete the entire anonymous process by multiple message passing and node value updating. Finally, we conduct experiments on real social network datasets to evaluate the DPKDML, The experimental results show that our methods may overcome the shortcomings of traditional methods in processing massive data, and reduce anonymous costs and increase data utility.
Online publication date: Tue, 22-Jan-2019
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of High Performance Computing and Networking (IJHPCN):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com